visual data
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visual data
learning, conceptualizing + communicating data with infographics, visualizations, etc...
Curated by Lauren Moss
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The Science Behind Data Visualisation

The Science Behind Data Visualisation | visual data |

'Over the last couple of centuries, data visualisation has developed to the point where it is in everyday use across all walks of life. Many recognise it as an effective tool for both storytelling and analysis, overcoming most language and educational barriers. But why is this? How are abstract shapes and colours often able to communicate large amounts of data more effectively than a table of numbers or paragraphs of text?

An understanding of human perception will not only answer this question, but will also provide clear guidance and tools for improving the design of your own visualisations.

In order to understand how we are able to interpret data visualisations so effectively, we must start by examining the basics of how we perceive and process information, in particular visual information.'

Ignasi Alcalde's curator insight, October 11, 2013 5:02 AM

De la memoria icónica a la memoria visual.

Paul P Roberts's curator insight, October 11, 2013 12:55 PM

Interesting article, how our brain see data, possible implication for how mobile research apps are designed

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Illusions in Data Visualization

Illusions in Data Visualization | visual data |

Data visualizations are effective ways for inputting information into a human’s brain, and some even state that visualizations are what makes our world real.

But even when the people who created the visualization are being honest, we can’t always trust what our eyes are showing us. We’ve evolved our visual perceptual system over millions of years (some other animals see optical illusions too) and it is extremely effective at what it does, but it still has some quirks.

In a data visualization context, illusions are dangerous because they can make us see things that aren’t really there in the data. Good practice helps us to avoid these optical illusions, but occasionally they can still sneak in through design choices, or just quirks in the way data lines up.

There are two main types of optical illusions: Physiological and Cognitive. When designing data visualizations, it can be useful to be aware of these illusions and keep an eye out for them...

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